Why Machine Learning Cannot Ignore Maximum Likelihood Estimation
Abstract
The growth of machine learning as a field has been accelerating with increasing interest and publications across fields, including statistics, but predominantly in computer science. How can we parse this vast literature for developments that exemplify the necessary rigor? How many of these manuscripts incorporate foundational theory to allow for statistical inference? Which advances have the greatest potential for impact in practice? One could posit many answers to these queries. Here, we assert that one essential idea is for machine learning to integrate maximum likelihood for estimation of functional parameters, such as prediction functions and conditional densities.
Cite
@article{arxiv.2110.12112,
title = {Why Machine Learning Cannot Ignore Maximum Likelihood Estimation},
author = {Mark J. van der Laan and Sherri Rose},
journal= {arXiv preprint arXiv:2110.12112},
year = {2021}
}
Comments
30 pages. Forthcoming as a chapter in the Handbook of Matching and Weighting in Causal Inference